Implementing arbitrage-free models for pricing convertible bonds

Imperial Users onl

Interdisciplinary Design and Analysis of a Warehouse

This project simulates the design and construction processes necessary to successfully repurpose an existing building. It compares the renovation of an existing warehouse into a school to the ground up construction of a similarly designed school. The comparison is based on the investigation of architectural, fire protection, structural, environmental, sustainability, and economical requirements. These factors guided recommendations on the opportunities provided in the renovation of existing buildings, the limitations, and the best practices used to overcome those constraints

Model-driven analysis of gene expression control

During this PhD, I worked on three different aspects in the broad field of experimental and theoretical analysis of gene regulation. The first part, "Quantifying the strength of miRNA-target interactions", addresses the problem of predicting mRNA targets of miRNAs. I show that biochemical measurements of miRNA-mRNA interactions can be used to optimise the parameter inference of a pre-existing model of miRNA target prediction. This model named MIRZA, predicts miRNA-mRNA binding using 25 energy parameters that describe the miRNA-mRNA hybrid structure, with 2 base pairing parameters for the AU and GC pairs, 3 configuration parameters for the symmetric and asymmetric loops, and 21 positional parameters for the 21 nucleotides of the miRNA sequence. MIRZA was built to infer these parameters from Argonaute protein CLIP data, which captures potential targets of miRNAs. Upon the publication of precise measurements of chemical kinetic constants of miRNA-mRNA binding interactions between a mRNA target and a set of systematically mutated miRNA sequences, we reasoned that such data could be used to improve the parameters inference of the MIRZA model. After showing that the prediction of the existing model on the set of measured miRNA-mRNA pairs shows high correlation with the binding energy calculated from the measurements, I used simulations as a proof of principle of the inference procedure and to design measurements that would be needed to infer the parameters of the MIRZA model. Staying in the field of miRNA, in "Single cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction", I developed an approach to infer miRNA targets based on scRNA-seq data from cells that express the miRNA at different levels. A miRNA can target several hundreds of different mRNAs and is present in the cell in limited quantities, implying that the interaction of a target mRNA with a specific miRNA depends on its concentration and on the interactions of the miRNA with its other targets. In other words, since miRNA binding is exclusive, mRNA targets compete for the same miRNA pool. Therefore, the concentrations of the thereby coupled mRNAs depend not only on the miRNA concentration but also on the concentration of every competing mRNA that is targeted by the same miRNA. To study this, HEK 293 cell lines were constructed to inducibly express a miRNA (hsa-miR-199a) as well as the mRNA encoding a green fluorescent protein. Express from the same promoter as the miRNA, this mRNA allows the monitoring of the miRNA concentration. The study aimed not only to determine the parameters of individual mRNA-mRNA interactions, but also to assess the degree to which mRNAs act in a competitive manner to influence each other's expression. scRNA-seq was chosen to bring the resolution needed to reach these goals. The effect of the miRNA on a bound target is to increase its decay rate, hence the expression levels of the targets depends on the miRNA concentration and their binding energy. To gain insight into the target binding energy, we constructed a model considering mRNA transcription rate, the miRNA-mRNA binding/unbinding rate, the mRNA decay rates in the bound and unbound state, and the free/bound concentration of miRNA. We showed that the model can be factored in terms of the miRNA concentrations in individual cells and the miRNA-mRNA target interaction parameters and we solved the model to obtain estimates of miRNA-mRNA interaction parameters, which we showed explain the mRNA levels in cells more accurately than the sequence-based computationally predicted interaction energies. Finally, in "Bayesian inference of the gene expression states from single-cell RNA-seq data" I carried out fundamental technical work on the normalisation of count data obtained in scRNA-seq experiments. As introduced above, multiple strategies have been developed with the aim of reducing the high level of noise present on such data, and estimating a 'true' biological state of expression for each gene in each cell. While the project aimed to reconstruct the Waddington landscape of regulator activity based on the single cell gene expression measurements, at the start of the project we realised that there is no satisfactory solution to gene expression normalisation in single cells in the literature. Thus, we tackled this problem with a Bayesian model, considering each gene independently and inferring a posterior probability of gene expression in each cell. Our model assumes a log-normal distribution of gene expression across cells and additional Poisson noise caused by the stochastic process of gene expression and the sampling process introduced by the mRNA capture in experimental protocols. These normalised gene expression values are the basis of a motif-activity response based approach for inferring the activity of TFs and miRNAs in individual cells, and for reconstructing the underlying landscape. The application of this normalisation algorithm to reconstruct a landscape is presented in the last part, "Realizing Waddington’s metaphor: Inferring regulatory landscapes from single-cell gene expression data". There I present the mathematical principles needed to formally define a landscape following the idea of Waddington from 1957, and I propose two applications of the landscape. First I show that it defines cell types as local minima, and secondly, in the case of cells undergoing differentiation, I show how the landscape can be used to find developmental path and the transcription factors associated with the differentiation process

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 24th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 28 regular papers presented in this volume were carefully reviewed and selected from 88 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

ELB-trees an efficient and lock-free B-tree derivative

This technical report is an extension of the paper of the same title, which is to appear at MUCOCOS'13. The technical report proves correctness of the ELB-trees operations' semantics and that the operations are lock-free

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

OCM 2021 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field